A state-of-the-art AI/ML model has been developed collaboratively by the Department of Science and Technology Centre of Excellence in Climate Modeling at the Indian Institute of Technology in Delhi (IIT-Delhi), the Indraprastha Institute of Information Technology (IIIT-Delhi), and universities in the United States and Japan. This cutting-edge model accurately predicts monsoon rainfall, providing valuable information for sectors such as agriculture and water resource management. The AI/ML model predicts an All India Summer Monsoon Rainfall (AISMR) of approximately 790 mm for the upcoming monsoon season, indicating a normal monsoon this year. To make this prediction, the model utilized historical AISMR data, the Niño3.4 index, and categorical Indian Ocean Dipole (IOD) data from 1901 to 2001. Its performance surpasses that of existing physical models used for monsoon predictions in the country, achieving an impressive forecast success rate of 61.9% during the test period from 2002 to 2022. The model can make forecasts several months in advance, contingent upon the availability of Niño3.4 index and IOD forecasts. These inputs can be continuously updated to reflect evolving conditions. The data-driven models offer flexibility and better capture the nonlinear relationships among monsoon drivers, while being computationally efficient. Accurate monsoon predictions have significant implications for critical decision-making across multiple socio-economic sectors, including agriculture planning, energy resource management, water resource utilisation, disaster management, and addressing health concerns. The techniques developed in this study will also be extended to provide state-wise monsoon rainfall predictions, enhancing their regional applications. Rainfall erosivity, a key factor in soil degradation, affects approximately 68.4% of eroded soil in India. Globally, rainfall-induced soil erosion poses a significant environmental challenge. However, traditional assessments in India are often limited to specific catchments or regions, hindering a comprehensive evaluation in a geographically diverse country like India. To address this, a study at IIT-Delhi conducted the first-ever pan-India assessment of rainfall erosivity. By utilizing multiple national and global gridded precipitation datasets, the researchers created a high-resolution map identifying erosion-prone areas in India. This step contributes to building a national-scale soil erosion model, enabling watershed managers to identify and prioritize locations for essential watershed development activities to mitigate soil erosion.